基于说话人特征时间一致性与分布的合成语音检测
Synthetic Speech Detection Based on Temporal Consistency and Distribution of Speaker Features
AI总结：
该研究分析 TTS 合成语音中说话人特征的时间一致性与分布缺陷，并据此提出低复杂度 SSD 方法，在跨数据集和静音裁剪场景下表现良好。
摘要：
当前的合成语音检测（SSD）方法在某些数据集上表现良好，但仍面临鲁棒性和可解释性问题。一个可能的原因是这些方法没有分析合成语音的缺陷。本文分析了文本到语音（TTS）过程中固有的说话人特征缺陷。由于 TTS 缺乏对说话人特征的细粒度控制，话语内说话人特征的时间一致性会出现差异。由于 TTS 中的说话人表示基于编码器提取的说话人嵌入，合成语音与真实语音之间话语间说话人特征的分布存在差异。基于这些分析，本文提出了一种基于说话人特征时间一致性与分布的 SSD 方法。一方面，对话语内说话人特征的时间一致性进行建模可辅助语音反欺骗；另一方面，可利用话语间说话人特征的分布差异进行 SSD。所提方法计算复杂度较低，并且在跨数据集和静音裁剪场景中均表现良好。
英文摘要：
Current synthetic speech detection (SSD) methods perform well on certain datasets but still face issues of robustness and interpretability. A possible reason is that these methods do not analyze the deficiencies of synthetic speech. In this paper, the flaws of the speaker features inherent in the text-to-speech (TTS) process are analyzed. Differences in the temporal consistency of intra-utterance speaker features arise due to the lack of fine-grained control over speaker features in TTS. Since the speaker representations in TTS are based on speaker embeddings extracted by encoders, the distribution of inter-utterance speaker features differs between synthetic and bonafide speech. Based on these analyzes, an SSD method based on temporal consistency and distribution of speaker features is proposed. On one hand, modeling the temporal consistency of intra-utterance speaker features can aid speech anti-spoofing. On the other hand, distribution differences in inter-utterance speaker features can be utilized for SSD. The proposed method offers low computational complexity and performs well in both cross-dataset and silence trimming scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
